Stronger Together? Linked Fate and Voter Preferences in the 2020 Election

A. Jordan Nafa, Meredith Walsh Niezgoda, P. DeAnne Roark, and Valerie Martinez-Ebers

University of North Texas

September 15th, 2022

Introduction

  • How do intersecting identities influence candidate preference among members of pan-ethnic racial groups?

    • Do these relationships vary within pan-ethnic identity groups?
  • We examine differences within and between pan-ethnic identity groups

    • Demonstrate the possibility of moving beyond “Latinos and Asian Americans are not monolithic” and treating heterogeneity among pan-ethnic groups as substantively interesting

Previous Research

Social identities in the Era of Trump

  • Race, ethnicity, gender, and social identity theory

  • What makes some identities more salient than others?

  • The Trump presidency and the salience of group identity

  • Theoretical Expectations

    • The salience of both pan-ethnic and gender-based dimensions of linked fate will vary across different sub-groups

    • The threat against multiple identities will activate intersectional linked fate

    • Relationship between intersectional linked fate and candidate preference is greater than the gender or pan-ethnic dimensions alone

Methods and Data

Data

Asian (N = 3,898) and Latino (N = 3,950) Respondents on the 2020 Collaborative Multi-Racial Post-Election Survey (Frasure et al. 2021)

  • Dependent Variable

    \(y_{i} = \begin{cases}1 \text{ if respondent } i \text{ Supported Trump in 2020}\\ 0 \text{ Otherwise}\end{cases}\)

  • Dimensions of Linked Fate: Pan-Ethnic, Gender, and Intersectional

    • “How much do you think what happens to the following groups here in the U.S. will have something to do with what happens in your life? What happens to [identity group] will have…”
  • Sex, Citizenship, Age, Education, Ancestry, Partisan Identification, percentage University educated, Median Age

Multilevel Regression and Post-Stratification (MrP)

MrP Model Specification

\[ \begin{aligned} y &\sim \mathrm{Binomial}(n, k, \theta)\\ \mathrm{logit}(\theta) &= \alpha + \beta_{1}\text{Female}_{i} + \beta_{2}\text{Citizen}_{i} + \upsilon_{j[i]}^{\mathrm{Ancestry}} + \upsilon_{l[i]}^{\mathrm{Education}} + \upsilon_{m[i]}^{\mathrm{Age}} +\\ &\quad \upsilon_{o[i]}^{\mathrm{Partisan}} + \upsilon_{p[i]}^{\mathrm{Linked~Fate}} + \upsilon_{q[i],p[i]}^{\mathrm{Female \times Linked~Fate}} + \upsilon_{q[i],j[i]}^{\mathrm{Female \times Ancestry}} +\\ &\quad \upsilon_{p[i],j[i]}^{\mathrm{Linked~Fate \times Ancestry}}\\ \text{where}\\ \upsilon_{j}^{\mathrm{Ancestry}} &\sim \mathcal{N}(\left[\gamma_{0} + \gamma_{1}\text{% College}_{j} + \gamma_{2}\text{Median Age}_{j} \right], ~\sigma^{\mathrm{Ancestry}}) \quad \text{for } j \in \{1, 2, \dots, J\}\\ \upsilon_{l}^{\mathrm{Education}} &\sim \mathcal{N}(0, ~\sigma^{\mathrm{Education}}) \quad \text{for } l \in \{1, 2, \dots, L\}\\ \upsilon_{m}^{\mathrm{Age}}&\sim \mathcal{N}(0, ~\sigma^{\mathrm{Age}}) \quad \text{for } m \in \{1, 2, \dots, M\}\\ \upsilon_{o}^{\mathrm{Partisan}} &\sim \mathcal{N}(0, ~\sigma^{\mathrm{Partisan}}) \quad \text{for } o \in \{1, 2, \dots, O\}\\ \upsilon_{p}^{\mathrm{Linked~Fate}} &\sim \mathcal{N}(0, ~\sigma^{\mathrm{Linked~Fate}})\quad \text{for } p \in \{1, 2, \dots, P\}\\ \upsilon_{q,p}^{\mathrm{Female \times Linked~Fate}} &\sim \mathcal{N}(0, ~\sigma^{\mathrm{Female \times Linked~Fate}}) \quad \text{for } q \in \{1, 2, \dots, Q\} \text{ and } p \in \{1, 2, \dots, P\}\\ \upsilon_{q,j}^{\mathrm{Female \times Ancestry}} &\sim \mathcal{N}(0, ~\sigma^{\mathrm{Female \times Ancestry}}) \quad \text{for } q \in \{1, 2\} \text{ and } j \in \{1, 2, \dots, J\}\\ \upsilon_{p,j}^{\mathrm{Linked~Fate \times Ancestry}}&\sim \mathcal{N}(0, ~\sigma^{\mathrm{Linked~Fate \times Ancestry}}) \quad \text{for } p \in \{1, 2, \dots, P\} \text{ and } j \in \{1, 2, \dots, J\} \end{aligned} \]

Post-Stratification Stage

Post-Stratified estimate by Linked Fate and Ancestry or Sex based on the 2019 American Community Survey 5-year Integrated Public Use Microdata Series data (Ruggles et al. 2022)

\[\begin{aligned}\theta^{\mathrm{MrP}} = \frac{\sum_{p,j \in P,J}N_{p,j}\cdot \theta_{p,j}}{\sum_{p,j \in P,J}N_{p,j}}\end{aligned}\]

  • \(\theta\) is the predicted probability of support for Trump from our model

  • \(N\) is the observed cell count in the post-stratification table

  • Summing over the population predictions within each pair of cells \(p,j \in P,J\) and dividing by the population total yields \(\theta^{\mathrm{MrP}}\) which represents the census-adjusted parameter estimate

Results

Conclusions

Main Findings

  • People who feel more connected to other “[men/women] of color” were overall less likely to support Trump in 2020

  • Relationship among Asian Americans is very non-linear across each dimension of Linked Fate, and particularly among those of East Asian descent

  • Pan-Ethnic identity groups are far from monolithic and this heterogeneity is substantively interesting

  • Not much evidence of within group-gender gaps in the joint distribution of linked fate and support for Trump across all dimensions

Conclusions and Limitations

  • We illustrate the application of MrP, the “gold standard for subnational public opinion estimation” (Lax and Phillips 2009), need not be limited to geography

  • Broad applicability in the racial and ethnic politics literature, particularly in descriptive work but can also be used to improve estimates in experimental research (see Gao, Kennedy, and Simpson 2022)

  • Our data is purely cross-sectional and constitutes a single snapshot in time—our aims here are purely descriptive

  • We cannot speak to the question of why we observe these within and between group differences in the relationships between linked fate and support for Trump as that is a causal question with a causal answer

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